Eye on AI

→ WHAT IT COVERS Oren Michaels, cofounder of Barndoor AI, explains why enterprises deploying AI agents face a governance crisis at scale. A 1,000-person company could generate 100,000 agents, each requiring distinct permissions. Barndoor provides a control layer between agents and enterprise tools, enabling safe autonomous action without rebuilding rules for every new AI model.

→ WHAT IT COVERS Tom Chen, Chief Product Officer at Aircall, covers how AI voice agents are transforming customer communications for small and mid-sized businesses. Aircall operates across 10+ global offices, handles 100 concurrent AI calls per line, and positions voice as a competitive advantage previously too costly for most companies. → KEY INSIGHTS - **AI Deployment Entry Point:** Start AI voice agents with after-hours and overflow call handling before touching core business hours...

→ WHAT IT COVERS Risto Miikkulainen, VP of AI Research at Cognizant AI Lab and UT Austin professor, explains how evolutionary computation — specifically population-based search, neuroevolution, and evolution strategies — solves problems that gradient descent cannot, enabling creative AI solutions across finance, medicine, scientific discovery, and multi-agent decision-making systems.

→ WHAT IT COVERS Chia-Lin Simmons, CEO of LogicMark, explains how the company's Freedom Alert Max device uses on-device AI, digital twins, and predictive analytics to shift elder care from reactive emergency response to proactive fall prevention, targeting the 90% of adults over 50 who want to age at home. → KEY INSIGHTS - **Fall Detection Personalization:** AI algorithms on the Freedom Alert Max device learn individual behavioral patterns to eliminate false positives.

→ WHAT IT COVERS Mitel CTO Luiz Domingos outlines how enterprise communications is being transformed by AI across contact centers, unified communications, and agentic workflows, arguing that voice will replace traditional screen-based interfaces and that hybrid edge architectures are becoming essential for regulated industries managing latency and data sovereignty.

→ WHAT IT COVERS Dr. Terry Sejnowski, Salk Institute neuroscientist and Boltzmann machine co-creator, examines whether ChatGPT genuinely understands language, identifies the 100-plus brain components absent from current AI systems, and outlines nature-inspired directions for developing more autonomous, capable AI agents. → KEY INSIGHTS - **Understanding is multi-dimensional:** Resist applying a single standard when evaluating whether AI "understands.

→ WHAT IT COVERS Eamonn Maguire of Proton explains how data profiling begins before a child is born, how AI models are trained on scraped data without consent, and how Proton's ecosystem — including Lumo AI, encrypted email, and the Born Private initiative — offers a structural alternative to surveillance-based platforms. → KEY INSIGHTS - **Pre-birth data profiling:** The moment a parent emails a gynecologist or fertility clinic using Gmail or Outlook, advertising platforms flag that household...

→ WHAT IT COVERS Steffen Cruz, CTO of Macrocosmos, explains how his company uses BitTensor's blockchain infrastructure to train large language models through distributed compute nodes worldwide, targeting 5,000 nodes by mid-2025 and 70-billion-parameter models as a commercial milestone for cost-arbitrage AI training. → KEY INSIGHTS - **Distributed Pretraining Economics:** Centralized data centers lock training costs into upfront capital expenditure, but distributed training enables real-time...

→ WHAT IT COVERS Errol Gardner, EY's global consulting leader, examines where agentic AI actually stands in enterprise adoption, why human resistance — not technology — blocks deployment, and how large organizations like EY's 400,000-person firm are navigating GenAI and agentic workflows in real production environments. → KEY INSIGHTS - **Agentic AI adoption scale:** Enterprise agentic AI sits below 1 out of 10 on an adoption scale, with only roughly 20% of organizations using it in any...

→ WHAT IT COVERS IBM Quantum VP Oliver Dial explains where quantum computing stands in 2026, covering the distinction between quantum utility and quantum advantage, how 156-qubit superconducting processors work, why the new Gross error-correcting code reduces qubit overhead by 10x, and why fault-tolerant systems are now projected for 2029. → KEY INSIGHTS - **Quantum Advantage Threshold:** IBM's Quantum Advantage Tracker, a public GitHub-based leaderboard modeled on Hugging Face, allows...

→ WHAT IT COVERS Kris Lovejoy, global strategy leader at Kyndryl (IBM's IT infrastructure spinoff), explains why agentic AI adoption in enterprises will take until roughly 2031 to reach scale, citing infrastructure gaps, security risks, compliance demands, and the absence of horizontal workflow integration across business functions. → KEY INSIGHTS - **Agentic AI Adoption Timeline:** Lovejoy predicts that by 2031, approximately half of traditional IT systems administration tasks — specifically...

→ WHAT IT COVERS Loris Degioanni, CTO and founder of Sysdig, explains how AI has compressed cyberattack timelines from weeks to hours, why traditional human-centered security is no longer sufficient, and how Sysdig's "headless cloud security" model built for AI agents represents the next defensive paradigm. → KEY INSIGHTS - **Attack timeline compression:** AI has reduced the window between vulnerability disclosure and active exploitation from weeks to hours.

→ WHAT IT COVERS Professor Andrew Thangaraj of IIT Madras details how the institute built a sub-$5,000 online BSc Data Science degree serving 40,000 active students, addressing India's broken higher education pipeline where only 27% of college-age youth enroll and quality degrees cost upward of $25,000. → KEY INSIGHTS - **Skills-before-theory curriculum design:** The IIT Madras BS Data Science program front-loads practical skills in its diploma phase, requiring students to build two functional...

→ WHAT IT COVERS Celia Merzbacher, executive director of the Quantum Economic Development Consortium (QEDC), presents data from five years of quantum industry surveys, covering market growth rates, enterprise readiness, quantum sensing, the AI-quantum intersection, and realistic timelines toward utility-scale quantum computing across sectors including pharmaceuticals, finance, and energy.

→ WHAT IT COVERS Steffen Cruz, CTO of Macrocosmos, explains how his company uses BitTensor's blockchain infrastructure to train large language models through distributed compute nodes worldwide, eliminating the need for centralized data centers and enabling cost arbitrage through surplus energy and idle consumer hardware like Mac minis and spare GPUs.

→ WHAT IT COVERS Eamonn Maguire, Head of AI at Proton, explains how data profiles are built on individuals before birth through email metadata and behavioral tracking, and how Proton's Born Private initiative and encrypted ecosystem — including Lumo AI, ProtonMail, and Proton Workspace — aim to counter this surveillance infrastructure. → KEY INSIGHTS - **Data profile construction:** Companies like Google build behavioral profiles from as few as three data points — an Instagram signup, a...

→ WHAT IT COVERS IBM Research India Director Amith Singhee examines why India has lagged in AI development despite abundant engineering talent, what conditions must converge for India to compete globally, and how IBM's enterprise-focused AI research — spanning hybrid cloud deployment, Granite LLMs, COBOL modernization, and agentic systems — addresses real-world business constraints.

→ WHAT IT COVERS Debdas Sen, CEO of TCG Digital, explains how his firm deploys hybrid AI combining proprietary knowledge graphs, enterprise data, and external LLMs to solve high-stakes industrial problems in energy and life sciences, arguing that AI without measurable ROI risks repeating the collapse seen after the 1990s hype cycle. → KEY INSIGHTS - **ROI threshold as project filter:** TCG Digital applies a 10x return benchmark when scoping client engagements — if a client spends $5M, the...

→ WHAT IT COVERS Professor Mausam of IIT Delhi analyzes why India lags behind the US and China in AI development despite having 1.4 billion people and elite technical institutions. He examines faculty shortages, funding diffusion, compute delays, brain drain, and government initiatives, arguing that systemic change—starting with attracting top professors—is the prerequisite for building a genuine AI ecosystem.

→ WHAT IT COVERS IBM Research VP Sriram Raghavan explains why IBM trains its Granite models — currently 2B and 8B parameters — directly using reinforcement learning rather than distilling from larger models, and how combining direct RL training with inference-time scaling allows small models to match GPT-4o and Claude 3.5 on code and math benchmarks at a fraction of the cost. → KEY INSIGHTS - **Direct RL vs.